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Being the “React guy” or the “Rust guy” used to be a moat. In 2027, it’s not. In this episode I break down the five bets I’m making on what developers should learn and do to stay employable as AI reshapes the job. We cover: Why going deep on one language or framework no longer gives you leverageBecoming the trusted human who cuts through AI hype inside your companyWhy teaching and explaining tech to non-technical people is a career superpowerAI engineering: agents, RAG, vector databases, MCP servers and tool callingCleaning up AI-generated slop for founders whose vibe-coded apps break at 5 usersThe rise of the internal forward-deployed engineer (and how to do it without the title)My take: stop going deep, go wide. Learn how businesses make money, get outside the LinkedIn and X bubble, and be ready for a world where regular people want to hire you to fix the software they built. Want to become an AI engineer, or need help with your app? Head to parsity.io or book a short call with me here: https://calendly.com/brianjenney83/15-minute-meeting-your-web-dev-roadmap-clone

I took over ownership of a coding bootcamp in 2023, just before being laid off as an engineering manager at Clorox. The last few years have been rough. In between working full time and rebuilding after being laid off, I had to restructure Parsity from the ground up.If you're a developer and worried about what AI might do to your own career, perhaps you'll find this useful and if you're interested in becoming an AI engineer, apply for 1 of 15 spots at https://parsity.io/ai-dev

Become an AI engineer in 45 days: https://parsity.io/ai-dev Luke Miller, founder of DCNY, joins the Develop Yourself podcast to share how his 40+ person engineering team uses agentic engineering to build and deploy software at remarkable speed and scale for nearly 200 clients. We talk about what agentic engineering actually looks like in practice, how to maintain quality while moving faster, why engineers need to become more product-minded, and what developers should focus on as AI continues to reshape software careers. Luke also shares his perspective on vibe-coded apps, hiring agentic engineers, and why the most valuable skill may simply be learning how to build things that are genuinely useful. If you’re a developer trying to understand where AI is taking software engineering—and how to adapt without losing yourself in predictions—this episode is for you.

Dallas Davenport is founder of Aivara, an AI automation marketing agency. We met in a Skool group when I offered to look at her app, and I fully expected a vibe-coded dumpster fire (I've dunked on plenty of those). Instead it was complex, it already had users, and it was pretty freaking good. The app is dealership software that replies to customer messages, pulls live inventory off the lot, and automates a bunch of other stuff car dealers deal with. She built it with Claude and she's not a software developer (and doesn't want to be). We talk about how she learned Claude well enough to pull that off, why her site barely mentions AI (dealers just want to sell more cars), how she gets B2B customers without social media, and how she'd vet a developer when she can't read the code herself.

Quite simply, I lay out EXACTLY what I'd build if I were trying to set myself up as an AI engineer. The type of project, the concepts and the tools to learn and why. I genuinely believe that in 2-3 years, the people who learn this stuff will have a massive advantage in the market. You can pick up the project here: https://www.parsity.io/ai-eng-side-project

Want to get into the top 10% of your engineering team by leveraging agentic engineering workflows? I'm planning a 2 day workshop here: https://form.typeform.com/to/EDuBEz4U I shipped a feature with AI in record time. Then I actually read the code. 60+ files. Weird abstractions. Tests that technically passed but proved almost nothing. Components rebuilt from scratch for no good reason. What looked like a finished feature was probably 70–80% of the way there. And the worst part? This wasn’t my first time doing this. I’ve also sat in a code review as a senior engineer and realized I couldn’t properly explain code that “I” had just written. I use Claude and Codex constantly. I’m not going back to hand-writing everything. But after using AI to build software at startups, inside my own business, and on large production systems, I think we’re learning the wrong lesson from how fast these tools can code. The question isn’t whether AI can write the code. It’s how much you need to understand before you’re willing to ship it.

I'm opening 12 spots for a weekend workshop in October for devs who want to ship quality software with agents. Sign up here: https://form.typeform.com/to/EDuBEz4U "You're absolutely right, I should have caught that" I'm one of the top 10% of AI users in a 1,000-person engineering org this year. I'm not an AI hater, I ship with these tools daily and I think they're genuinely fun to use. But I've also watched "customers don't care about the code" turn into an excuse for engineers to stop thinking entirely, and that's how you end up with 5,000 lines nobody can explain, a catastrophic bug in prod, and Claude cheerfully saying on the way down. Let's explore ways to maintain quality software without becoming a "meat proxy"

Become an AI Engineer at parsity.io I recruit for AI roles on the side and I pulled real, live forward-deployed engineer postings and broke down exactly what they're screening for. Turns out it's not what most "learn AI" content tells you. What's actually in these postings: Real applied AI experience: shipped production agents, not tutorial chatbotsRAG, retrieval quality, evals, and agent-to-agent communicationSurprisingly low years-of-experience requirements compared to typical senior rolesThe ability to explain technical tradeoffs to non-technical stakeholdersVoice AI showing up more and more as a differentiator — mentioned in this episode: AI Voice Pioneers (https://www.skool.com/pioneers) and Retell AI (https://www.retellai.com)Degrees mattering a lot less than you'd think, at least in the Bay AreaWant to actually build one of these skills instead of just reading about it? Free RAG project: parsity.io/ai-with-rag If you're trying to reverse-engineer your way into one of these roles, this is what the actual postings are telling you to build toward.

We teach AI systems engineering at Parsity: parsity.io In this one I get into why senior is mostly a comp band with a name on it, the three things every senior actually needs to know/have, the big fish / small pond strategy I've been running for seven years, and the single move that gets you promoted faster than getting better at your language. Also: why you should stop replaying that interview where somebody corrected you.

Become an AI engineer in 45 days. Join Parsity: https://parsity.io Being a "coder" is getting less and less about coding. I've been writing code for 12 years and I'm watching skills I worked hard for get automated. Counter-intuitively, I believe a new brand of software developer is going to be even more valuable and in demand going forward. Your existing skills aren't disappearing but the ones that matter the most certainly are. Book a call with me here: https://calendly.com/brianjenney83/15-minute-meeting-your-web-dev-roadmap-clone

No one is hiring. AI took your job. Junior developers aren't needed anymore. I can't tell you how sick I am of hearing this but there is some truth to it. Things aren't roses and candy canes but they're not all doom and gloom either. Tom graduated from Parsity's coding bootcamp in 2025 and got hired faster than you might expect. There's no secret to his success but there are clues. He's got nothing to sell you but a lot to tell you.

Ryan Murphy spent five years as an engineering manager at Yelp on the team that owned the infrastructure behind every dollar moving through the company. He got the job the way a lot of people do, which is that his boss had no budget for a real engineering manager and asked if he'd try it. Most of this conversation is about the move from engineer to manager: what the job actually is, what he looks for before he'll move someone into it, and why he calls it a career change instead of a promotion. That last part is something I got told when I made the jump and completely ignored. I thought I was getting a bigger version of my old job. He also runs EM Accelerator, a training program for engineering managers, and Crying on the Sofa, a set of free mental health events for people in tech. He talks about the night that started the second one, sitting on his couch holding his newborn son. What we covered: The engineering manager archetypes, and how to spot which one a company is really hiring forWhat he watches during a production incident before he'll consider promoting youWhy the strongest engineer on the team is often the wrong pickWhy management is a career change, not a promotionWhy there's so little real training for new managers, and what he teaches instead of one-on-one templatesWhether a manager has to be regulated themselves to lead wellWhy he started Crying on the SofaWho should not become an engineering managerHis take on AI flattening orgs, and why he thinks people-focused managers matter more, not less Find Ryan: EM Accelerator: https://www.emaccelerator.com/Crying on the Sofa: https://www.cryingonthesofa.org/LinkedIn: https://www.linkedin.com/in/ryansmurphy1Newsletter: https://www.softwareengineeringtimes.com/

Most engineers have read a lot about AI and built almost nothing with it. That is not a character flaw. It is the default. Stanford's AI Index puts numbers on the gap: a tiny fraction of households actually pay for AI tools, while nearly every company claims to be using it. Somebody is lying, and it is not the people quietly getting on with their jobs. This episode is about closing that gap. Two levels of leverage, both available to you without permission from anyone. Level one is team strategy. Skills, plugins, and shared config so your team stops solving the same problem fifteen different ways. Level two is connecting these tools to things that actually matter at your company. Your data, your documentation, your internal systems, with a human still in the loop where it counts. Plus why running a hackathon is the fastest way to find the problem worth solving, and what happened when I did it. Want to build this stuff instead of reading about it? Parsity's AI engineering program: https://parsity.io/ai-dev

Want to actually build agents and RAG systems like the ones I mention in this episode? That's exactly what we do inside Parsity's AI engineering program: https://parsity.io/ai-dev I've been coding for nearly 12 years, I own and operate a coding bootcamp, and I've done over 1,000 free phone calls with people trying to break into tech. So this is the harsh advice I'd give you if you were my own kid. The stuff I can't really say on LinkedIn without getting flamed by people who've never worked a day in tech. The market for juniors is rough right now. That part is true. But most people trying to break in are doing the exact same thing as everyone else: building the same to-do app, obsessing over green dots on their GitHub, slapping "passionate junior developer" on their resume, and smashing LinkedIn easy-apply 1,000 times. That's your competition. And it's beatable. In this episode: Why "look junior, act passionate, wait for someone to take a chance on you" is a losing strategyHow to build real, verifiable experience when you have none (start an LLC, get actual users, stop building toy apps)The one skill that actually matters now, and why "I use Claude" is table stakes, not a differentiatorWhy agents and RAG are still wide open. Even senior and staff engineers mostly aren't doing this yetWhere the jobs actually are (small businesses and startups are hiring, big tech is slashing) and how to find them off LinkedInThe LinkedIn search-ranking trick most people ignoreWhy your timeline might take 2, 3, even 4 years, and why the ROI math still makes it worth itStop outsourcing your brain to ChatGPT before you've even built the skill Take it all with a grain of salt. It's one opinion. But I'll never tell you to do something I haven't done myself or seen work firsthand. Free resources I mention: Parsity AI Engineering Program: https://parsity.io/ai-dev Free 5-Day AI Advisory Board course (build agents + RAG hands-on): https://www.parsity.io/5-day-ai-advisory-boardFree RAG project (better than most paid ones): https://www.parsity.io/ai-with-ragThe agent project I mention: https://www.parsity.io/ai-agentGusto Small Business Jobs Report (hundreds of thousands of net new small-business jobs this year): https://gusto.com/resources/gusto-insights/smb-jobs-report-june-2026My video on the entry-level hiring collapse, "Entry-level jobs didn't disappear. They moved. Got a question or something you want me to cover? Drop it in the comments. I get tons on YouTube but almost none on Spotify, and this is where the hardcore Develop Yourself crew lives. Comment away, it genuinely helps the show get found.

Learn to build AI systems that companies actually pay for: RAG, agents, production LLM apps. Not YouTube tutorial toys. Real projects with a dedicated mentor who's shipped this stuff at scale. Spots are capped at 10 per cohort: https://parsity.io/ai-dev Who should software engineers marry? Not another software engineer, apparently. Samir Ranjan is the CTO and Director of Data Science at Catenate, where he built a patented platform that measures soft skills and personality to predict career outcomes. Translation: he has receipts on what actually gets engineers hired, promoted, and passed over. We get into: The 2 traits that separate good engineers from average ones (spoiler: it's not knowing every tool)Why "learning ability" beats technical knowledge in the AI eraThe age group struggling most with AI adoption (it's not who you think)What the data really says about junior devs getting replacedThe new job roles coming by 2027: AI orchestrators and agent managersWhy engineers should learn an art form (seriously)His weirdly practical advice for introverts: dress up and go to TargetWho software engineers should actually marry Samir went from mining engineering a thousand feet underground to building AI career tools. If you're trying to make sense of a chaotic job market, this one's full of stuff you can actually use. Connect with Samir on LinkedIn and check out Catenate. Connect with me: LinkedIn: https://www.linkedin.com/in/brianjenneyYouTube: https://www.youtube.com/@brianjenney